Optimal selection of parameters of a hybrid model of vehicle/passenger for prediction of head injury in front crash

Nowadays, a great number of researches are done by scientists to provide some models that can predict the passenger injuries in crashes. In this paper, a hybrid model of vehicle and passenger is proposed to predict the head acceleration in the front crash. A lumped mass model with 12-degree-of-freedom (DOF) is firstly used to predict the behavior of vehicle in front crash. In this model, any member of vehicle is modeled as a lumped mass and connected to the other members through some springs and dampers. The unknown coefficients of such model are obtained using genetic algorithm to minimize the deviation between the results of experimental and suggested model. The parameters of model are established by experimental results of a real world car, namely the HONDA ACORD2011, in an accident velocity of 48 km/h. Also, the validity of the proposed model is checked by experimental results of mentioned vehicle at two other crash velocities of 40 km/h, and 56 km/h. The results show that the proposed model is an efficient framework for preliminary designing of both structure and parameter design of vehicle to improve its crash worthiness. Moreover, a multi-body dynamic model of driver is proposed to predict the head injury in front crash. The seat acceleration which has been calculated using vehicle’s model is considered as input of this model.